With the growing awareness of data privacy, federated learning (FL) has gained increasing
attention in recent years as a major paradigm for training models with privacy protection in …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Federated Learning is a novel framework that allows multiple devices or institutions to train a
machine learning model collaboratively while preserving their data private. This …

We present RemixIT, a simple yet effective self-supervised method for training speech
enhancement without the need of a single isolated in-domain speech nor a noise waveform …

Federated learning has a significant advantage in protecting information privacy. Many
scholars proposed various secure learning methods within the framework of federated …

Intelligent equipment fault diagnosis based on federated transfer learning (FTL) attracts
considerable attention from both academia and industry. It allows real-world industrial …

Deploying machine learning applications on edge devices can bring clear benefits such as
improved reliability, latency and privacy but it also introduces its own set of challenges. Most …

Deep learning based speech enhancement in the short-time Fourier transform (STFT)
domain typically uses a large window length such as 32 ms. A larger window can lead to …

A promising approach for multi-microphone speech separation involves two deep neural
networks (DNN), where the predicted target speech from the first DNN is used to compute …

We propose RemixIT, a simple and novel self-supervised training method for speech
enhancement. The proposed method is based on a continuously self-training scheme that …